{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is helpful to compare different views of data side by side.\n",
    "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n",
    "These subplots might be insets, grids of plots, or other more complicated layouts.\n",
    "In this section we'll explore four routines for creating subplots in Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.axes``: Subplots by Hand\n",
    "\n",
    "The most basic method of creating an axes is to use the ``plt.axes`` function.\n",
    "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n",
    "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n",
    "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
    "\n",
    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GYAcAyxDsAGAZgh0ALEOwA4BlCHYAsAzBDgCWIdgBwDIEOwBYhmAHAMsQ7ABgGYIdACxD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\nBwDLEOwAYBmCHQAsQ7ADgGUIdgCwzP8AxZhVoScunCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd88240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.axes()  # standard axes\n",
    "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1A4qLaV4gJgZo0kS/x1AqgXfeAY4dAxYvBpYtAxo1AjZs0O9xmGmSfVIozcWF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\nmMb/A681T5mxFqFRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e296748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\n",
    "                   xticklabels=[], ylim=(-1.2, 1.2))\n",
    "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\n",
    "                   ylim=(-1.2, 1.2))\n",
    "\n",
    "x = np.linspace(0, 10)\n",
    "ax1.plot(np.sin(x))\n",
    "ax2.plot(np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplot``: Simple Grids of Subplots\n",
    "\n",
    "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n",
    "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n",
    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EuP/++0V9fb1tu8ViEePGjRMJCQkdHuNNufrcBUq1tbUICgrqcn99fT3mz5+P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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bec31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)),\n",
    "             fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n",
    "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q6+vx85//3O51LleoVCr87ne/Q19fH0pKSrBu3Tq89tprNn8uOnLz5k1s2bIF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\niYgkw8FORCQZDnYiIsn8H1BDee8pcOnKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be83ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(1, 7):\n",
    "    ax = fig.add_subplot(2, 3, i)\n",
    "    ax.text(0.5, 0.5, str((2, 3, i)),\n",
    "           fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplots``: The Whole Grid in One Go\n",
    "\n",
    "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
    "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
    "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
    "\n",
    "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1023b3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hISRJQmZmJlauXOnw+N7eXjQ0NGDSpEkYM2aMm0+JvOnGjRuwWq2YPXs2IiIi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\nvvmiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1023b3828>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
    "                      fontsize=18, ha='center')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.GridSpec``: More Complicated Arrangements\n",
    "\n",
    "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
    "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n",
    "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can specify subplot locations and extents using the familiary Python slicing syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f438128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(grid[0, 0])\n",
    "plt.subplot(grid[0, 1:])\n",
    "plt.subplot(grid[1, :2])\n",
    "plt.subplot(grid[1, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of flexible grid alignment has a wide range of uses.\n",
    "I most often use it when creating multi-axes histogram plots like the ones shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sbNzWYBSNRhkeHmZmZoaFhQXa29uJRqMsLS3h9/txu92kUimRh1e6Nbce9BqN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Icffoju7m7ExcUNv85Hw/JWoaFDJe7u7li0aNGEv3/p0qWTnOi+RYsWISoqatzba7VatLW1Ka46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\nsbyJiFSI5U1EpEL/A5Irq324HXUvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef598d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some normally distributed data\n",
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
    "\n",
    "# Set up the axes with gridspec\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
    "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
    "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
    "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
    "\n",
    "# scatter points on the main axes\n",
    "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
    "\n",
    "# histogram on the attached axes\n",
    "x_hist.hist(x, 40, histtype='stepfilled',\n",
    "            orientation='vertical', color='gray')\n",
    "x_hist.invert_yaxis()\n",
    "\n",
    "y_hist.hist(y, 40, histtype='stepfilled',\n",
    "            orientation='horizontal', color='gray')\n",
    "y_hist.invert_xaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
